Health Care Cost Reductions with Machine Learning-Directed Evaluations during Radiation Therapy - An Economic Analysis of a Randomized Controlled Study.

BACKGROUND: Machine learning (ML) may cost-effectively direct health care by identifying patients most likely to benefit from preventative interventions to avoid negative and expensive outcomes. System for High-Intensity Evaluation During Radiation Therapy (SHIELD-RT; NCT04277650) was a single-institution, randomized controlled study in which electronic health record-based ML accurately identified patients at high risk for acute care (emergency visit or hospitalization) during radiotherapy (RT) and targeted them for supplemental clinical evaluations. This ML-directed intervention resulted in decreased acute care utilization. Given the limited prospective data showing the ability of ML to direct interventions cost-efficiently, an economic analysis was performed. METHODS: A post hoc economic analysis was conducted of SHIELD-RT that included RT courses from January 7, 2019, to June 30, 2019. ML-identified high-risk courses (≥10% risk of acute care during RT) were randomized to receive standard of care weekly clinical evaluations with ad hoc supplemental evaluations per clinician discretion versus mandatory twice-weekly evaluations. The primary outcome was difference in mean total medical costs during and 15 days after RT. Acute care costs were obtained via institutional cost accounting. Physician and intervention costs were estimated via Medicare and Medicaid data. Negative binomial regression was used to estimate cost outcomes after adjustment for patient and disease factors. RESULTS: A total of 311 high-risk RT courses among 305 patients were randomized to the standard (n=157) or the intervention (n=154) group. Unadjusted mean intervention group supplemental visit costs were $155 per course (95% confidence interval, $142 to $168). The intervention group had fewer acute care visits per course (standard, 0.47; intervention, 0.31; P=0.04). Total mean adjusted costs were $3110 per course for the standard group and $1494 for the intervention group (difference in means, $1616 [95% confidence interval, $1450 to $1783]; P=0.03). CONCLUSIONS: In this economic analysis of a randomized controlled, health care ML study, mandatory supplemental evaluations for ML-identified high-risk patients were associated with both reduced total medical costs and improved clinical outcomes. Further study is needed to determine whether economic results are generalizable. (Funded in part by The Duke Endowment, The Conquer Cancer Foundation, the Duke Department of Radiation Oncology, and the National Cancer Institute of the National Institutes of Health [R01CA277782]; ClinicalTrials.gov number, NCT04277650.).

The Next Chapter in Immunotherapy and Radiation Combination Therapy: Cancer-Specific Perspectives.

Immunotherapeutic agents have revolutionized cancer treatment over the past decade. However, most patients fail to respond to immunotherapy alone. A growing body of preclinical studies highlights the potential for synergy between radiation therapy and immunotherapy, but the outcomes of clinical studies have been mixed. This review summarizes the current state of immunotherapy and radiation combination therapy across cancers, highlighting existing challenges and promising areas for future investigation.

Stereotactic body radiotherapy with or without selective dismutase mimetic in pancreatic adenocarcinoma: an adaptive, randomised, double-blind, placebo-controlled, phase 1b/2 trial.

BACKGROUND: Stereotactic body radiotherapy (SBRT) has the potential to ablate localised pancreatic ductal adenocarcinoma. Selective dismutase mimetics sensitise tumours while reducing normal tissue toxicity. This trial was designed to establish the efficacy and toxicity afforded by the selective dismutase mimetic avasopasem manganese when combined with ablative SBRT for localised pancreatic ductal adenocarcinoma. METHODS: In this adaptive, randomised, double-blind, placebo-controlled, phase 1b/2 trial, patients aged 18 years or older with borderline resectable or locally advanced pancreatic cancer who had received at least 3 months of chemotherapy and had an Eastern Cooperative Oncology Group performance status of 0-2 were enrolled at six academic sites in the USA. Eligible patients were randomly assigned (1:1), with block randomisation (block sizes of 6-12) with a maximum of 24 patients per group, to receive daily avasopasem (90 mg) or placebo intravenously directly before (ie, within 180 min) SBRT (50, 55, or 60 Gy in five fractions, adaptively assigned in real time by Bayesian estimates of 90-day safety and efficacy). Patients and physicians were masked to treatment group allocation, but not to SBRT dose. The primary objective was to find the optimal dose of SBRT with avasopasem or placebo as determined by the late onset EffTox method. All analyses were done on an intention-to-treat basis. This study is registered with ClinicalTrials.gov, NCT03340974, and is complete. FINDINGS: Between Jan 25, 2018, and April 29, 2020, 47 patients were screened, of whom 42 were enrolled (median age was 71 years [IQR 63-75], 23 [55%] were male, 19 [45%] were female, 37 [88%] were White, three [7%] were Black, and one [2%] each were unknown or other races) and randomly assigned to avasopasem (n=24) or placebo (n=18); the placebo group was terminated early after failing to meet prespecified efficacy parameters. At data cutoff (June 28, 2021), the avasopasem group satisfied boundaries for both efficacy and toxicity. Late onset EffTox efficacy response was observed in 16 (89%) of 18 patients at 50 Gy and six (100%) of six patients at 55 Gy in the avasopasem group, and was observed in three (50%) of six patients at 50 Gy and nine (75%) of 12 patients at 55 Gy in the placebo group, and the Bayesian model recommended 50 Gy or 55 Gy in five fractions with avasopasem for further study. Serious adverse events of any cause were reported in three (17%) of 18 patients in the placebo group and six (25%) of 24 in the avasopasem group. In the placebo group, grade 3 adverse events within 90 days of SBRT were abdominal pain, acute cholangitis, pyrexia, increased blood lactic acid, and increased lipase (one [6%] each); no grade 4 events occurred. In the avasopasem group, grade 3-4 adverse events within 90 days of SBRT were acute kidney injury, increased blood alkaline phosphatase, haematoma, colitis, gastric obstruction, lung infection, abdominal abscess, post-surgical atrial fibrillation, and pneumonia leading to respiratory failure (one [4%] each).There were no treatment-related deaths but one late death in the avasopasem group due to sepsis in the setting of duodenal obstruction after off-study treatment was reported as potentially related to SBRT. INTERPRETATION: SBRT that uses 50 or 55 Gy in five fractions can be considered for patients with localised pancreatic ductal adenocarcinoma. The addition of avasopasem might further enhance disease outcomes. A larger phase 2 trial (GRECO-2, NCT04698915) is underway to validate these results. FUNDING: Galera Therapeutics.

Comparing Survival After Resection, Ablation, and Radiation in Small Intrahepatic Cholangiocarcinoma.

BACKGROUND: Hepatectomy is the cornerstone of curative-intent treatment for intrahepatic cholangiocarcinoma (ICC). However, in patients unable to be resected, data comparing efficacy of alternatives including thermal ablation and radiation therapy (RT) remain limited. Herein, we compared survival between resection and other liver-directed therapies for small ICC within a national cancer registry. PATIENTS AND METHODS: Patients with clinical stage I-III ICC < 3 cm diagnosed 2010-2018 who underwent resection, ablation, or RT were identified in the National Cancer Database. Overall survival (OS) was compared using Kaplan-Meier and multivariable Cox proportional hazards methods. RESULTS: Of 545 patients, 297 (54.5%) underwent resection, 114 (20.9%) ablation, and 134 (24.6%) RT. Median OS was similar between resection and ablation [50.5 months, 95% confidence interval (CI) 37.5-73.9; 39.5 months, 95% CI 28.7-58.4, p = 0.14], both exceeding that of RT (20.9 months, 95% CI 14.1-28.3). RT patients had high rates of stage III disease (10.4% RT vs. 1.8% ablation vs. 11.8% resection, p < 0.001), but the lowest rates of chemotherapy utilization (9.0% RT vs. 15.8% ablation vs. 38.7% resection, p < 0.001). In multivariable analysis, resection and ablation were associated with reduced mortality compared with RT [hazard ratio (HR) 0.44, 95% CI 0.33-0.58 and HR 0.53, 95% CI 0.38-0.75, p < 0.001, respectively]. CONCLUSION: Resection and ablation were associated with improved survival in patients with ICC < 3 cm compared with RT. Acknowledging confounders, anatomic constraints of ablation, limitations of available data, and need for prospective study, these results favor ablation in small ICC where resection is not feasible.

NCCN Guidelines® Insights: Biliary Tract Cancers, Version 2.2023.

In 2023, the NCCN Guidelines for Hepatobiliary Cancers were divided into 2 separate guidelines: Hepatocellular Carcinoma and Biliary Tract Cancers. The NCCN Guidelines for Biliary Tract Cancers provide recommendations for the evaluation and comprehensive care of patients with gallbladder cancer, intrahepatic cholangiocarcinoma, and extrahepatic cholangiocarcinoma. The multidisciplinary panel of experts meets at least on an annual basis to review requests from internal and external entities as well as to evaluate new data on current and emerging therapies. These Guidelines Insights focus on some of the recent updates to the NCCN Guidelines for Biliary Tract Cancers as well as the newly published section on principles of molecular testing.

Healthcare provider evaluation of machine learning-directed care: reactions to deployment on a randomised controlled study.

OBJECTIVES: Clinical artificial intelligence and machine learning (ML) face barriers related to implementation and trust. There have been few prospective opportunities to evaluate these concerns. System for High Intensity EvaLuation During Radiotherapy (NCT03775265) was a randomised controlled study demonstrating that ML accurately directed clinical evaluations to reduce acute care during cancer radiotherapy. We characterised subsequent perceptions and barriers to implementation. METHODS: An anonymous 7-question Likert-type scale survey with optional free text was administered to multidisciplinary staff focused on workflow, agreement with ML and patient experience. RESULTS: 59/71 (83%) responded. 81% disagreed/strongly disagreed their workflow was disrupted. 67% agreed/strongly agreed patients undergoing intervention were high risk. 75% agreed/strongly agreed they would implement the ML approach routinely if the study was positive. Free-text feedback focused on patient education and ML predictions. CONCLUSIONS: Randomised data and firsthand experience support positive reception of clinical ML. Providers highlighted future priorities, including patient counselling and workflow optimisation.

Advances in the treatment of intrahepatic cholangiocarcinoma: An overview of the current and future therapeutic landscape for clinicians.

Intrahepatic cholangiocarcinoma (ICC) is the second most common primary liver tumor and remains a fatal malignancy in the majority of patients. Approximately 20%-30% of patients are eligible for resection, which is considered the only potentially curative treatment; and, after resection, a median survival of 53 months has been reported when sequenced with adjuvant capecitabine. For the 70%-80% of patients who present with locally unresectable or distant metastatic disease, systemic therapy may delay progression, but survival remains limited to approximately 1 year. For the past decade, doublet chemotherapy with gemcitabine and cisplatin has been considered the most effective first-line regimen, but results from the recent use of triplet regimens and even immunotherapy may shift the paradigm. More effective treatment strategies, including those that combine systemic therapy with locoregional therapies like radioembolization or hepatic artery infusion, have also been developed. Molecular therapies, including those that target fibroblast growth factor receptor and isocitrate dehydrogenase, have recently received US Food and Drug Administration approval for a defined role as second-line treatment for up to 40% of patients harboring these actionable genomic alterations, and whether they should be considered in the first-line setting is under investigation. Furthermore, as the oncology field seeks to expand indications for immunotherapy, recent data demonstrated that combining durvalumab with standard cytotoxic therapy improved survival in patients with ICC. This review focuses on the current and future strategies for ICC treatment, including a summary of the primary literature for each treatment modality and an algorithm that can be used to drive a personalized and multidisciplinary approach for patients with this challenging malignancy.

CT-derived body composition measurements as predictors for neoadjuvant treatment tolerance and survival in gastroesophageal adenocarcinoma.

PURPOSE: Treatment for gastroesophageal adenocarcinomas can result in significant morbidity and mortality. The purpose of this study is to supplement methods for choosing treatment strategy by assessing the relationship between CT-derived body composition, patient, and tumor features, and clinical outcomes in this population. METHODS: Patients with neoadjuvant treatment, biopsy-proven gastroesophageal adenocarcinoma, and initial staging CTs were retrospectively identified from institutional clinic encounters between 2000 and 2019. Details about patient, disease, treatment, and outcomes (including therapy tolerance and survival) were extracted from electronic medical records. A deep learning semantic segmentation algorithm was utilized to measure cross-sectional areas of skeletal muscle (SM), visceral fat (VF), and subcutaneous fat (SF) at the L3 vertebra level on staging CTs. Univariate and multivariate analyses were performed to assess the relationships between predictors and outcomes. RESULTS: 142 patients were evaluated. Median survival was 52 months. Univariate and multivariate analysis showed significant associations between treatment tolerance and SM and VF area, SM to fat and VF to SF ratios, and skeletal muscle index (SMI) (p = 0.004-0.04). Increased survival was associated with increased body mass index (BMI) (p = 0.01) and increased SMI (p = 0.004). A multivariate Cox model consisting of BMI, SMI, age, gender, and stage demonstrated that patients in the high-risk group had significantly lower survival (HR = 1.77, 95% CI = 1.13-2.78, p = 0.008). CONCLUSION: CT-based measures of body composition in patients with gastroesophageal adenocarcinoma may be independent predictors of treatment complications and survival and can supplement methods for assessing functional status during treatment planning.

Implementation of machine learning in the clinic: challenges and lessons in prospective deployment from the System for High Intensity EvaLuation During Radiation Therapy (SHIELD-RT) randomized controlled study.

BACKGROUND: Artificial intelligence (AI) and machine learning (ML) have resulted in significant enthusiasm for their promise in healthcare. Despite this, prospective randomized controlled trials and successful clinical implementation remain limited. One clinical application of ML is mitigation of the increased risk for acute care during outpatient cancer therapy. We previously reported the results of the System for High Intensity EvaLuation During Radiation Therapy (SHIELD-RT) study (NCT04277650), which was a prospective, randomized quality improvement study demonstrating that ML based on electronic health record (EHR) data can direct supplemental clinical evaluations and reduce the rate of acute care during cancer radiotherapy with and without chemotherapy. The objective of this study is to report the workflow and operational challenges encountered during ML implementation on the SHIELD-RT study. RESULTS: Data extraction and manual review steps in the workflow represented significant time commitments for implementation of clinical ML on a prospective, randomized study. Barriers include limited data availability through the standard clinical workflow and commercial products, the need to aggregate data from multiple sources, and logistical challenges from altering the standard clinical workflow to deliver adaptive care. CONCLUSIONS: The SHIELD-RT study was an early randomized controlled study which enabled assessment of barriers to clinical ML implementation, specifically those which leverage the EHR. These challenges build on a growing body of literature and may provide lessons for future healthcare ML adoption. TRIAL REGISTRATION: NCT04277650. Registered 20 February 2020. Retrospectively registered quality improvement study.

Comparing Outcomes of Oligometastases Treated with Hypofractionated Image-Guided Radiotherapy (HIGRT) with a Simultaneous Integrated Boost (SIB) Technique versus Metastasis Alone: A Multi-Institutional Analysis.

PURPOSE: We previously reported on the clinical outcomes of treating oligometastases with radiation using an elective simultaneous integrated boost technique (SIB), delivering higher doses to known metastases and reduced doses to adjacent bone or nodal basins. Here we compare outcomes of oligometastases receiving radiation targeting metastases alone (MA) versus those treated via an SIB. METHODS: Oligometastatic patients with ≤5 active metastases treated with either SIB or MA radiation at two institutions from 2013 to 2019 were analyzed retrospectively for treatment-related toxicity, pain control, and recurrence patterns. Tumor metastasis control (TMC) was defined as an absence of progression in the high dose planning target volume (PTV). Marginal recurrence (MR) was defined as recurrence outside the elective PTV but within the adjacent bone or nodal basin. Distant recurrence (DR) was defined as any recurrence that is not within the PTV or surrounding bone or nodal basin. The outcome rates were estimated using the Kaplan-Meier method and compared between the two techniques using the log-rank test. RESULTS: 101 patients were treated via an SIB to 90 sites (58% nodal and 42% osseous) and via MA radiation to 46 sites (22% nodal and 78% osseous). The median follow-up among surviving patients was 24.6 months (range 1.4-71.0). Of the patients treated to MA, the doses ranged from 18 Gy in one fraction (22%) to 50 Gy in 10 fractions (50%). Most patients treated with an SIB received 50 Gy to the treated metastases and 30 Gy to the elective PTV in 10 fractions (88%). No acute grade ≥3 toxicities occurred in either cohort. Late grade ≥3 toxicity occurred in 3 SIB patients (vocal cord paralysis and two vertebral body compression), all related to the high dose PTV and not the elective volume. There was similar crude pain relief between cohorts. The MR-free survival rate at 2 years was 87% (95% CI: 70%, 95%) in the MA group and 98% (95% CI: 87%, 99%) in the SIB group (p = 0.07). The crude TMC was 89% (41/46) in the MA group and 94% (85/90) in the SIB group. There were no significant differences in DR-free survival (65% (95% CI: 55-74%; p = 0.24)), disease-free survival (60% (95% CI: 40-75%; p = 0.40)), or overall survival (88% (95% CI: 73-95%; p = 0.26)), between the MA and SIB cohorts. CONCLUSION: Both SIB and MA irradiation of oligometastases achieved high rates of TMC and similar pain control, with a trend towards improved MR-free survival for oligometastases treated with an SIB. Further investigation of this technique with prospective trials is warranted.

Transfer learning for fluence map prediction in adrenal stereotactic body radiation therapy.

Objective:To design a deep transfer learning framework for modeling fluence map predictions for stereotactic body radiation therapy (SBRT) of adrenal cancer and similar sites that usually have a small number of cases.Approach:We developed a transfer learning framework for adrenal SBRT planning that leverages knowledge in a pancreas SBRT planning model. Treatment plans from the two sites had different dose prescriptions and beam settings but both prioritized gastrointestinal sparing. A base framework was first trained with 100 pancreas cases. This framework consists of two convolutional neural networks (CNN), which predict individual beam doses (BD-CNN) and fluence maps (FM-CNN) sequentially for 9-beam intensity-modulated radiation therapy (IMRT) plans. Forty-five adrenal plans were split into training/validation/test sets with the ratio of 20/10/15. The base BD-CNN was re-trained with transfer learning using 5/10/15/20 adrenal training cases to produce multiple candidate adrenal BD-CNN models. The base FM-CNN was directly used for adrenal cases. The deep learning (DL) plans were evaluated by several clinically relevant dosimetric endpoints, producing a percentage score relative to the clinical plans.Main results:Transfer learning significantly reduced the number of training cases and training time needed to train such a DL framework. The adrenal transfer learning model trained with 5/10/15/20 cases achieved validation plan scores of 85.4/91.2/90.7/89.4, suggesting that model performance saturated with 10 training cases. Meanwhile, a model using all 20 adrenal training cases without transfer learning only scored 80.5. For the final test set, the 5/10/15/20-case models achieved scores of 73.5/75.3/78.9/83.3.Significance:It is feasible to use deep transfer learning to train an IMRT fluence prediction framework. This technique could adapt to different dose prescriptions and beam configurations. This framework potentially enables DL modeling for clinical sites that have a limited dataset, either due to few cases or due to rapid technology evolution.

Deep Learning-Based Fluence Map Prediction for Pancreas Stereotactic Body Radiation Therapy With Simultaneous Integrated Boost.

PURPOSE: Treatment planning for pancreas stereotactic body radiation therapy (SBRT) is a challenging task, especially with simultaneous integrated boost treatment approaches. We propose a deep learning (DL) framework to accurately predict fluence maps from patient anatomy and directly generate intensity modulated radiation therapy plans. METHODS AND MATERIALS: The framework employs 2 convolutional neural networks (CNNs) to sequentially generate beam dose prediction and fluence map prediction, creating a deliverable 9-beam intensity modulated radiation therapy plan. Within the beam dose prediction CNN, axial slices of combined structure contour masks are used to predict 3-dimensional (3D) beam doses for each beam. Each 3D beam dose is projected along its beam's-eye-view to form a 2D beam dose map, which is subsequently used by the fluence map prediction CNN to predict its fluence map. Finally, the 9 predicted fluence maps are imported into the treatment planning system to finalize the plan by leaf sequencing and dose calculation. One hundred patients receiving pancreas SBRT were retrospectively collected for this study. Benchmark plans with unified simultaneous integrated boost prescription (25/33 Gy) were manually optimized for each case. The data set was split into 80/20 cases for training and testing. We evaluated the proposed DL framework by assessing both the fluence maps and the final predicted plans. Further, clinical acceptability of the plans was evaluated by a physician specializing in gastrointestinal cancer. RESULTS: The DL-based planning was, on average, completed in under 2 minutes. In testing, the predicted plans achieved similar dose distribution compared with the benchmark plans (-1.5% deviation for planning target volume 33 V33Gy), with slightly higher planning target volume maximum (+1.03 Gy) and organ at risk maximum (+0.95 Gy) doses. After renormalization, the physician rated 19 cases clinically acceptable and 1 case requiring minor improvement. CONCLUSIONS: The DL framework can effectively plan pancreas SBRT cases within 2 minutes. The predicted plans are clinically deliverable, with plan quality approaching that of manual planning.

Hepatobiliary Cancers, Version 2.2021, NCCN Clinical Practice Guidelines in Oncology.

The NCCN Guidelines for Hepatobiliary Cancers focus on the screening, diagnosis, staging, treatment, and management of hepatocellular carcinoma (HCC), gallbladder cancer, and cancer of the bile ducts (intrahepatic and extrahepatic cholangiocarcinoma). Due to the multiple modalities that can be used to treat the disease and the complications that can arise from comorbid liver dysfunction, a multidisciplinary evaluation is essential for determining an optimal treatment strategy. A multidisciplinary team should include hepatologists, diagnostic radiologists, interventional radiologists, surgeons, medical oncologists, and pathologists with hepatobiliary cancer expertise. In addition to surgery, transplant, and intra-arterial therapies, there have been great advances in the systemic treatment of HCC. Until recently, sorafenib was the only systemic therapy option for patients with advanced HCC. In 2020, the combination of atezolizumab and bevacizumab became the first regimen to show superior survival to sorafenib, gaining it FDA approval as a new frontline standard regimen for unresectable or metastatic HCC. This article discusses the NCCN Guidelines recommendations for HCC.

An Interpretable Planning Bot for Pancreas Stereotactic Body Radiation Therapy.

PURPOSE: Pancreas stereotactic body radiation therapy (SBRT) treatment planning requires planners to make sequential, time-consuming interactions with the treatment planning system to reach the optimal dose distribution. We sought to develop a reinforcement learning (RL)-based planning bot to systematically address complex tradeoffs and achieve high plan quality consistently and efficiently. METHODS AND MATERIALS: The focus of pancreas SBRT planning is finding a balance between organ-at-risk sparing and planning target volume (PTV) coverage. Planners evaluate dose distributions and make planning adjustments to optimize PTV coverage while adhering to organ-at-risk dose constraints. We formulated such interactions between the planner and treatment planning system into a finite-horizon RL model. First, planning status features were evaluated based on human planners' experience and defined as planning states. Second, planning actions were defined to represent steps that planners would commonly implement to address different planning needs. Finally, we derived a reward system based on an objective function guided by physician-assigned constraints. The planning bot trained itself with 48 plans augmented from 16 previously treated patients, and generated plans for 24 cases in a separate validation set. RESULTS: All 24 bot-generated plans achieved similar PTV coverages compared with clinical plans while satisfying all clinical planning constraints. Moreover, the knowledge learned by the bot could be visualized and interpreted as consistent with human planning knowledge, and the knowledge maps learned in separate training sessions were consistent, indicating reproducibility of the learning process. CONCLUSIONS: We developed a planning bot that generates high-quality treatment plans for pancreas SBRT. We demonstrated that the training phase of the bot is tractable and reproducible, and the knowledge acquired is interpretable. As a result, the RL planning bot can potentially be incorporated into the clinical workflow and reduce planning inefficiencies.

Maximizing Tumor Control and Limiting Complications With Stereotactic Body Radiation Therapy for Pancreatic Cancer.

PURPOSE: Stereotactic body radiation therapy (SBRT) and stereotactic ablative body radiation therapy is being increasingly used for pancreatic cancer (PCa), particularly in patients with locally advanced and borderline resectable disease. A wide variety of dose fractionation schemes have been reported in the literature. This HyTEC review uses tumor control probability models to evaluate the comparative effectiveness of the various SBRT treatment regimens used in the treatment of patients with localized PCa. METHODS AND MATERIALS: A PubMed search was performed to review the published literature on the use of hypofractionated SBRT (usually in 1-5 fractions) for PCa in various clinical scenarios (eg, preoperative [neoadjuvant], borderline resectable, and locally advanced PCa). The linear quadratic model with α/ß= 10 Gy was used to address differences in fractionation. Logistic tumor control probability models were generated using maximum likelihood parameter fitting. RESULTS: After converting to 3-fraction equivalent doses, the pooled reported data and associated models suggests that 1-year local control (LC) without surgery is ≈79% to 86% after the equivalent of 30 to 36 Gy in 3 fractions, showing a dose response in the range of 25 to 36 Gy, and decreasing to less than 70% 1-year LC at doses below 24 Gy in 3 fractions. The 33 Gy in 5 fraction regimen (Alliance A021501) corresponds to 28.2 Gy in 3 fractions, for which the HyTEC pooled model had 77% 1-year LC without surgery. Above an equivalent dose of 28 Gy in 3 fractions, with margin-negative resection the 1-year LC exceeded 90%. CONCLUSIONS: Pooled analyses of reported tumor control probabilities for commonly used SBRT dose-fractionation schedules for PCa suggests a dose response. These findings should be viewed with caution given the challenges and limitations of this review. Additional data are needed to better understand the dose or fractionation-response of SBRT for PCa.

Ipilimumab and Radiation in Patients with High-risk Resected or Regionally Advanced Melanoma.

PURPOSE: In this prospective trial, we sought to assess the feasibility of concurrent administration of ipilimumab and radiation as adjuvant, neoadjuvant, or definitive therapy in patients with regionally advanced melanoma. PATIENTS AND METHODS: Twenty-four patients in two cohorts were enrolled and received ipilimumab at 3 mg/kg every 3 weeks for four doses in conjunction with radiation; median dose was 4,000 cGy (interquartile range, 3,550-4,800 cGy). Patients in cohort 1 were treated adjuvantly; patients in cohort 2 were treated either neoadjuvantly or as definitive therapy. RESULTS: Adverse event profiles were consistent with those previously reported with checkpoint inhibition and radiation. For the neoadjuvant/definitive cohort, the objective response rate was 64% (80% confidence interval, 40%-83%), with 4 of 10 evaluable patients achieving a radiographic complete response. An additional 3 patients in this cohort had a partial response and went on to surgical resection. With 2 years of follow-up, the 6-, 12-, and 24-month relapse-free survival for the adjuvant cohort was 85%, 69%, and 62%, respectively. At 2 years, all patients in the neoadjuvant/definitive cohort and 10/13 patients in the adjuvant cohort were still alive. Correlative studies suggested that response in some patients were associated with specific CD4+ T-cell subsets. CONCLUSIONS: Overall, concurrent administration of ipilimumab and radiation was feasible, and resulted in a high response rate, converting some patients with unresectable disease into surgical candidates. Additional studies to investigate the combination of radiation and checkpoint inhibitor therapy are warranted.